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Record W3193308838 · doi:10.47133/nemityra010203

Audible minorities and discrimination: an overview

2021· article· es· W3193308838 on OpenAlexaff
Erwin J. Warkentin

Bibliographic record

VenueÑEMITỸRÃ Revista Multilingüe de Lengüa Sociedad y Educación · 2021
Typearticle
Languagees
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsPronunciationIntelligibility (philosophy)PsychologyPerceptionFirst languageLinguisticsSecond languageSocial psychologyEpistemology

Abstract

fetched live from OpenAlex

This article deals with research into the importance of native or near native pronunciation of a person's L2 or L3 and the prejudices faced by those who may speak an accented form of a majority language. In order to reach its conclusions, the article analyses research focused on the learning of English as an L2 or L3 undertaken over the last 50 years. It concentrated on the impact of accented speech and the biases for and against accented speakers of a majority language and identifies where these findings converge and diverge. These results also indicate that the concerns of researchers have shifted as the perceptions of foreigners within English-speaking societies have changed over time. Ingroups are identified as important in determining the relative values of accents and that accented speakers constitute an audible minority and exert pressure on its members to maintain the "accentedness" of their speech. In defining the nature of an audible minority, the article indicates that this can be as powerful a factor as that of skin colour in some societies. In the end, it questions whether meticulous uniformity in spoken language is important or even desirable. The article concludes by suggesting that the problem of the audible minority is more that of the listener than the speaker since many of the myths surrounding accentedness and intelligibility have been proven false.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.394
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueÑEMITỸRÃ Revista Multilingüe de Lengüa Sociedad y EducaciónSame topicLinguistic Variation and MorphologyFrench-language works237,207